ValueError：X 有 63 个特征，但 SVC 期望 31 个特征作为输入

Question

我正在开发一个使用 ACO-SVM 的手语识别系统，有这个错误“X 有 63 个特征，但 SVC 期望 31 个特征作为输入”。这是我的代码

请问我正在使用 ACO-SVM 开发手语识别系统，上面的标题有错误，有人可以帮助我吗？当我尝试使用预测功能时它显示错误

# Load the dataset
df = pd.read_csv('dataset.csv')
df.columns = [i for i in range(df.shape[1])]

# Split the data into features and labels
X = df.iloc[:, :-1]
Y = df.iloc[:, -1]

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

# Apply feature scaling to the training and testing data
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_test_scaled = scaler.transform(x_test)

# Define the ACO-SVM model
class HybridACOSVM:
    def __init__(self, svm_params, aco_params):
        self.svm_params = svm_params
        self.aco_params = aco_params
        self.svm = None

    def train(self, x_train, y_train):
        # Perform ACO feature selection
        selected_features = self.perform_aco(x_train, y_train)

        # Train SVM using selected features
        self.svm = SVC(**self.svm_params)
        self.svm.fit(x_train[:, selected_features], y_train)

    def perform_aco(self, x_train, y_train):
        # Perform ACO feature selection algorithm
        # Your ACO algorithm implementation here

        # Randomly select some features as a placeholder
        num_features = x_train.shape[1]
        num_selected = int(num_features * 0.5)  # Select half of the features
        selected_features = np.random.choice(num_features, num_selected, replace=False)

        return selected_features

    def predict(self, x_test):
        # Make predictions using the trained SVM model
        return self.svm.predict(x_test)

# Set the parameters for SVM and ACO
svm_params = {
    'C': 10,
    'gamma': 0.1,
    'kernel': 'rbf'
}

aco_params = {
    # Parameters for the ACO algorithm
    # Adjust these parameters according to your implementation
}

# Train the hybrid ACO-SVM model
hybrid_model = HybridACOSVM(svm_params, aco_params)
hybrid_model.train(x_train_scaled, y_train)

# Make predictions on the test set
y_pred = hybrid_model.predict(x_test_scaled)```